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160 lines
5.1 KiB
160 lines
5.1 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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g_array_size = 102400
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class TestDGCOp(unittest.TestCase):
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def setup(self, place, array_size=g_array_size):
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size = array_size
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np.random.seed(5) # fix seed
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self.scope = fluid.global_scope()
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self.place = place
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print("place:", place)
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# numpy data
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# inputs: U, V, Grad, current_step, nranks
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self.u_name = "U"
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self.u = np.random.random(size).astype("float32")
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self.v_name = "V"
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self.v = np.random.random(size).astype("float32")
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self.grad_name = "Grad"
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self.grad = np.random.random(size).astype("float32")
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self.param_name = "Param"
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self.param = np.random.random(size).astype("float32")
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self.current_step_name = "current_step"
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self.current_step = np.full((1), 0.0).astype("float32")
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self.nranks_name = "nranks"
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self.nranks = np.full((1), 2.0).astype("float32")
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# output: U_out, V_out, EncodeGrad, GradLocal_out, k, GatherBuff
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self.encode_grad_name = "EncodeGrad"
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self.k_name = "k"
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self.k = np.full((1), 0.0).astype("float32")
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self.gather_buff_name = "GatherBuff"
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# scope data
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self.u_tensor = self.scope.var(self.u_name).get_tensor()
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self.u_tensor.set(self.u, place)
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self.v_tensor = self.scope.var(self.v_name).get_tensor()
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self.v_tensor.set(self.v, place)
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self.grad_tensor = self.scope.var(self.grad_name).get_tensor()
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self.grad_tensor.set(self.grad, place)
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self.param_tensor = self.scope.var(self.param_name).get_tensor()
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self.param_tensor.set(self.param, place)
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self.current_step_tensor = self.scope.var(
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self.current_step_name).get_tensor()
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self.current_step_tensor.set(self.current_step, core.CPUPlace())
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self.nranks_tensor = self.scope.var(self.nranks_name).get_tensor()
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self.nranks_tensor.set(self.nranks, core.CPUPlace())
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self.encode_grad_tensor = self.scope.var(
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self.encode_grad_name).get_tensor()
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self.k_tensor = self.scope.var(self.k_name).get_tensor()
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self.k_tensor.set(self.k, core.CPUPlace())
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self.gather_buff_tensor = self.scope.var(
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self.gather_buff_name).get_tensor()
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def check(self, actual_t, expect_t, place, out_name, atol=1e-5):
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self.assertTrue(
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np.allclose(
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actual_t, expect_t, atol=atol),
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"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
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+ str(expect_t) + "\n" + "But Got" + str(actual_t))
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def test_run_and_check(self):
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self.setup(place=core.CUDAPlace(0))
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kwargs = {
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# inputs
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'U': self.u_name,
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'V': self.v_name,
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'Grad': self.grad_name,
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'Param': self.param_name,
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'current_step': self.current_step_name,
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'nranks': self.nranks_name,
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# outputs
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'U_out': self.u_name,
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'V_out': self.v_name,
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'EncodeGrad': self.encode_grad_name,
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'Grad_out': self.grad_name,
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'k': self.k_name,
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'GatherBuff': self.gather_buff_name,
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# attrs
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'm': 0.9,
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'sparsity': [0.75, 0.9375, 0.984375, 0.996, 0.999],
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'use_nesterov': True,
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'rampup_begin_step': float(0.0),
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'rampup_step': float(10.0),
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'regular_coeff': float(1e-4),
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'regular_type': int(2),
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}
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dgc_op = Operator('dgc', **kwargs)
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#atol = 1e-6
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dgc_op.run(self.scope, self.place)
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u_out = np.array(self.u_tensor)
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v_out = np.array(self.v_tensor)
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grad_out = np.array(self.grad_tensor)
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encode_grad_out = np.array(self.encode_grad_tensor)
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k = int(np.array(self.k_tensor)[0])
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print("u_out:", u_out[0:20])
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print("v_out:", v_out[0:20])
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print("encode_grad_out:", encode_grad_out)
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print("k_out:", k)
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self.assertEqual(k, int(g_array_size * 0.25))
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index = encode_grad_out[0:k].view(dtype=np.int32)
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value = encode_grad_out[k:2 * k]
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acl = 1e-7
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for i in range(0, k):
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self.assertAlmostEqual(u_out[index[i]], 0.0)
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self.assertAlmostEqual(v_out[index[i]], 0.0)
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a_min = np.amin(value)
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dangling = [x for x in v_out if x > a_min]
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if __name__ == "__main__":
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unittest.main()
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